"""simple docstring"""



import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

SCREAMING_SNAKE_CASE__:Dict									=			{
    """b0""": {
        """hidden_dim""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}



def 	_lowerCamelCase(	a				):
       __a       =    EfficientNetConfig()
       __a       =    CONFIG_MAP[model_name]["hidden_dim"]
       __a       =    CONFIG_MAP[model_name]["width_coef"]
       __a       =    CONFIG_MAP[model_name]["depth_coef"]
       __a       =    CONFIG_MAP[model_name]["image_size"]
       __a       =    CONFIG_MAP[model_name]["dropout_rate"]
       __a       =    CONFIG_MAP[model_name]["dw_padding"]

       __a       =    "huggingface/label-files"
       __a       =    "imagenet-1k-id2label.json"
       __a       =    1_0_0_0
       __a       =    json.load(open(hf_hub_download(a   ,    a   ,    repo_type="dataset"				)   ,    "r"				)				)
       __a       =    {int(a				): v for k, v in idalabel.items()}

       __a       =    idalabel
       __a       =    {v: k for k, v in idalabel.items()}
       return config



def 	_lowerCamelCase(	):
       __a       =    "http://images.cocodataset.org/val2017/000000039769.jpg"
       __a       =    Image.open(requests.get(a   ,    stream=a				).raw				)
       return im



def 	_lowerCamelCase(	a				):
       __a       =    CONFIG_MAP[model_name]["image_size"]
       __a       =    EfficientNetImageProcessor(
           size={"height": size, "width": size}   ,    image_mean=[0.4_85, 0.4_56, 0.4_06]   ,    image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]   ,    do_center_crop=a   ,    )
       return preprocessor



def 	_lowerCamelCase(	a				):
       __a       =    [v.split("_"				)[0].split("block"				)[1] for v in original_param_names if v.startswith("block"				)]
       __a       =    sorted(set(a				)				)
       __a       =    len(a				)
       __a       =    {b: str(a				) for b, i in zip(a   ,    range(a				)				)}

       __a       =    []
       rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")				)
       rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")				)
       rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")				)
       rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")				)
       rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")				)

       for b in block_names:
              __a       =    block_name_mapping[b]
              rename_keys.append((F"block{b}_expand_conv/kernel:0", F"encoder.blocks.{hf_b}.expansion.expand_conv.weight")				)
              rename_keys.append((F"block{b}_expand_bn/gamma:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.weight")				)
              rename_keys.append((F"block{b}_expand_bn/beta:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.bias")				)
              rename_keys.append(
                  (F"block{b}_expand_bn/moving_mean:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")				)
              rename_keys.append(
                  (F"block{b}_expand_bn/moving_variance:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")				)
              rename_keys.append(
                  (F"block{b}_dwconv/depthwise_kernel:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")				)
              rename_keys.append((F"block{b}_bn/gamma:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")				)
              rename_keys.append((F"block{b}_bn/beta:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")				)
              rename_keys.append(
                  (F"block{b}_bn/moving_mean:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")				)
              rename_keys.append(
                  (F"block{b}_bn/moving_variance:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")				)

              rename_keys.append((F"block{b}_se_reduce/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")				)
              rename_keys.append((F"block{b}_se_reduce/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")				)
              rename_keys.append((F"block{b}_se_expand/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")				)
              rename_keys.append((F"block{b}_se_expand/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")				)
              rename_keys.append(
                  (F"block{b}_project_conv/kernel:0", F"encoder.blocks.{hf_b}.projection.project_conv.weight")				)
              rename_keys.append((F"block{b}_project_bn/gamma:0", F"encoder.blocks.{hf_b}.projection.project_bn.weight")				)
              rename_keys.append((F"block{b}_project_bn/beta:0", F"encoder.blocks.{hf_b}.projection.project_bn.bias")				)
              rename_keys.append(
                  (F"block{b}_project_bn/moving_mean:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_mean")				)
              rename_keys.append(
                  (F"block{b}_project_bn/moving_variance:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_var")				)

       rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight")				)
       rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight")				)
       rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias")				)
       rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean")				)
       rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var")				)

       __a       =    {}
       for item in rename_keys:
              if item[0] in original_param_names:
                     __a       =    "efficientnet." + item[1]

       __a       =    "classifier.weight"
       __a       =    "classifier.bias"
       return key_mapping



def 	_lowerCamelCase(	a   ,    a   ,    a				):
       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

              __a       =    key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     __a       =    torch.from_numpy(a				).permute(3   ,    2   ,    0   ,    1				)
              elif "depthwise_kernel" in key:
                     __a       =    torch.from_numpy(a				).permute(2   ,    3   ,    0   ,    1				)
              elif "kernel" in key:
                     __a       =    torch.from_numpy(np.transpose(a				)				)
              else:
                     __a       =    torch.from_numpy(a				)

              # Replace HF parameters with original TF model parameters
              assert hf_params[hf_key].shape == new_hf_value.shape
              hf_params[hf_key].copy_(a				)



@torch.no_grad()
def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a				):
       __a       =    model_classes[model_name](
           include_top=a   ,    weights="imagenet"   ,    input_tensor=a   ,    input_shape=a   ,    pooling=a   ,    classes=1_0_0_0   ,    classifier_activation="softmax"   ,    )

       __a       =    original_model.trainable_variables
       __a       =    original_model.non_trainable_variables
       __a       =    {param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              __a       =    param.numpy()
       __a       =    list(tf_params.keys()				)

       # Load HuggingFace model
       __a       =    get_efficientnet_config(a				)
       __a       =    EfficientNetForImageClassification(a				).eval()
       __a       =    hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print("Converting parameters..."				)
       __a       =    rename_keys(a				)
       replace_params(a   ,    a   ,    a				)

       # Initialize preprocessor and preprocess input image
       __a       =    convert_image_processor(a				)
       __a       =    preprocessor(images=prepare_img()   ,    return_tensors="pt"				)

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              __a       =    hf_model(**a				)
       __a       =    outputs.logits.detach().numpy()

       # Original model inference
       __a       =    False
       __a       =    CONFIG_MAP[model_name]["image_size"]
       __a       =    prepare_img().resize((image_size, image_size)   ,    resample=PIL.Image.NEAREST				)
       __a       =    image.img_to_array(a				)
       __a       =    np.expand_dims(a   ,    axis=0				)
       __a       =    original_model.predict(a				)

       # Check whether original and HF model outputs match  -> np.allclose
       assert np.allclose(a   ,    a   ,    atol=1E-3				), "The predicted logits are not the same."
       print("Model outputs match!"				)

       if save_model:
              # Create folder to save model
              if not os.path.isdir(a				):
                     os.mkdir(a				)
              # Save converted model and image processor
              hf_model.save_pretrained(a				)
              preprocessor.save_pretrained(a				)

       if push_to_hub:
              # Push model and image processor to hub
              print(F"Pushing converted {model_name} to the hub..."				)
              __a       =    F"efficientnet-{model_name}"
              preprocessor.push_to_hub(a				)
              hf_model.push_to_hub(a				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:int									=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""b0""",
       type=str,
       help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default="""hf_model""",
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
   parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

   SCREAMING_SNAKE_CASE__:Optional[int]									=			parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)

"""simple docstring"""



from __future__ import annotations

import math



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    u
       for i in range(1   ,    a				):
              __a       =    temp * (u - i)
       return temp



def 	_lowerCamelCase(	):
       __a       =    int(input("enter the numbers of values: "				)				)
       __a       =    []
       for _ in range(a				):
              y.append([]				)
       for i in range(a				):
              for j in range(a				):
                     y[i].append(a				)
                     __a       =    0

       print("enter the values of parameters in a list: "				)
       __a       =    list(map(a   ,    input().split()				)				)

       print("enter the values of corresponding parameters: "				)
       for i in range(a				):
              __a       =    float(input()				)

       __a       =    int(input("enter the value to interpolate: "				)				)
       __a       =    (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1   ,    a				):
              for j in range(n - i				):
                     __a       =    y[j + 1][i - 1] - y[j][i - 1]

       __a       =    y[0][0]
       for i in range(1   ,    a				):
              summ += (ucal(a   ,    a				) * y[0][i]) / math.factorial(a				)

       print(F"the value at {value} is {summ}"				)


if __name__ == "__main__":
   main()
